supervised-fine-tuning

Learn How To Observe, Manage, and Scale, Agentic AI Apps Using Azure AI Foundry - with this hands-on workshop

[ICML 2025] Official code for the paper "RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models"

Code for SFT and RL

LoRA fine-tuning pipeline for tool-calling chat LLMs with config-driven datasets, deterministic prompts, and built-in tool-call evaluation.

[ICML 2025] Official code for the paper "RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models"

Automatic music tagging using foundation models

🎯 Fine-tuning LLMs using LlamaFactory for financial intent understanding | Evaluating open-source models on OpenFinData benchmark | Full implementation with multiple models (Qwen2.5/ChatGLM3/Baichuan2/Llama3)

Open-source research engineering project for building the end-to-end post-training stack for reasoning language models, including SFT, preference learning, RLHF/RLVR, evaluation, inference-time scaling, and scalable systems for frontier-level reasoning.

Fine-tuned Meta's LLaMA 3.2 1B for text summarization using QLoRA (4-bit quantization + LoRA), achieving 40%+ improvement in ROUGE-2 over the base model on CNN/DailyMail dataset.

Supervised Fine Tuning with QLoRA

🦙 Llama2-FineTuning: Fine-tune LLAMA 2 with Custom Datasets Using LoRA and QLoRA Techniques

Fine-tuning various Llama 3.1 family of models on the Mult-It dataset

End-to-end Supervised Fine-Tuning (SFT) pipeline for TinyLlama-1.1B-Chat, specialized in trademark similarity risk assessment using heuristic-labeled SFT data, CPU-only LoRA training, adapter validation, full-weight merge, GGUF export, quantization (Q4_K_M), and local inference deployment via llama.cpp.

Supervised fine-tuning (SFT) of Qwen3 for structured medical reasoning QA — teaching models to "think before answering" (<think>...</think>). Supports full fine-tuning & LoRA, with an end-to-end data→train→eval→compare pipeline and quantitative benchmarks (PPL, format compliance, semantic similarity, latency/throughput).

Compact TensorFlow language model for Election Commission of India (ECI) domain pretraining and assistant-masked SFT.

Fine-tuning Llama-3 8B using Unsloth & QLoRA to automate SME customer service logic with 99% accuracy.

Fine-tune Qwen2.5-VL-7B with LoRA to predict human-rated emotion intensity (1–7) from images, with a ResNet18 regression baseline, full preprocessing/SFT pipeline, and evaluation (MAE/RMSE + bias analysis).

End-to-end 35.5M-parameter small language model in medical domain built from scratch in PyTorch with retention-aware continual pretraining, masked SFT, exact resume, and sealed evaluation.

Minimal masked diffusion LM in PyTorch. Pretraining + SFT + a scaling family from 50M to 350M, LLaDA recipe.

Fine-tune Qwen3-0.6B for resume parsing using LoRA